Journal article

Reinforcement learning enabled dynamic bidding strategy for instant delivery trading

Chaojie Guo, Russell G Thompson, Greg Foliente, Xiaoshuai Peng

COMPUTERS & INDUSTRIAL ENGINEERING | PERGAMON-ELSEVIER SCIENCE LTD | Published : 2021

Abstract

Due to the great potential to enable collaboration and improve consolidation, auctions have been identified as a possible effective option to improve the efficiency of instant delivery. Instant delivery markets are complex and dynamic systems influenced by highly random demand. Conventional bidding strategies require perfect market information and cannot be adjusted effectively according to the evolution of requests. To address this problem, this paper proposes an auction-based trading platform to enable freight transportation procurement and develops a Reinforcement Learning (RL) enabled dynamic bidding strategy to optimize carrier's behavior in sequential auctions. In the RL enabled dynami..

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